Delta and Omicron: protective measures and SARS-CoV-2 infections in day care centres in Germany in the 4th and 5th wave of the pandemic 2021/2022

Background During the five waves of the SARS-CoV-2 pandemic so far, German early childhood education and care (ECEC) centres implemented various protective measures, such as wearing a face mask, fixed children-staff groups or regular ventilation. In addition, parents and ECEC staff were increasingly vaccinated throughout 2021. During the 4th wave, variant of concern (VOC) Delta-driven transmission indicators reached record values at the end of 2021. Those values were even exceeded in the 5th wave at the beginning of 2022 when Omicron dominated. We examine which factors facilitated or prevented infection with SARS-CoV-2 in ECEC centres, and if these differed between different phases within wave 4 (Delta) and 5 (Omicron). Methods Since August 2020, a weekly online survey among approximately 8000 ECEC managers has been conducted, monitoring both incident SARS-CoV-2 infections and protective measures taken. We included data from calendar week 26/2021 to 05/2022. We estimate the probability of any infections and the number of SARS-CoV-2 infections in children, parents and staff using random-effect-within-between (REWB) panel models for binomial and count data. Results While children, parents and staff of ECEC centres with a high proportion of children from families with low socioeconomic status (SES) have a higher risk of infections in the beginning of wave 4 (OR up to 1.99 [1.56; 2.56]), this effect diminishes for children and parents with rising incidences. Protective measures, such as wearing face masks, tend to have more extensive effects with rising incidences in wave 5 (IRR up to 0.87 [0.8; 0.93]). Further, the protective effect of vaccination against infection among staff is decreasing from wave 4 to wave 5 (OR 0.3 [0.16; 0.55] to OR 0.95, [0.84; 1.07, n.s.]). The degree of transmission from staff to child and from staff to parent is decreasing from wave 4 to wave 5, while transmission from child to staff seems to increase. Conclusion While Omicron seems to affect children and parents from ECEC centres with families with all SES levels more equally than Delta, the protective effect of vaccination against infection is decreasing and the effect of protective measures like face masks becomes increasingly important. In order to prevent massive closures of ECEC centres due to infection of staff, protective measures should be strictly adhered to, especially to protect staff in centres with a high proportion of children from families with low socioeconomic status. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-022-14521-x.

Further Analysis: In-depth analysis and selection of hygiene variables As mentioned above, we excluded several hygiene measures from the main analysis, namely surface disinfection, group separation outdoors, fixed staff assignment and temperature measurement for staff and children. In the following, we provide a brief discussion of the whys and wherefores of this decision and provide an in-depth analysis of the excluded items. Doing so, we replicate our analyses in different settings. In the first setting, we use summed indices for certain hygiene measures (index setting, see Figure A7), e.g. for testing and contact reduction, hence a more parsemoneous model. In the second setting, we include all individual hygiene measurement variables (See Figure A8), hence, we use a more generous approach (generous setting). The variable selection in the model presented in the paper is based on considerations from both approaches. In the following, we provide a brief discussion of both approaches.
• Appendix Figure A7 is a replication of Figure 4 using sum indices for different groups of hygiene measeres, cumulative effects of similar measures are assumed here (index setting). Here, single dichotomous hygiene items are summed up by thematic groups as described below. The idea behind that approach was to get rid of the high correlations between some items, especially between the contact variables. Appendix Figure A2 provides correlation coefficients for all hygiene measurements for all waves, showing that group separation indoors has a .6 correlation with fixed staff assignment and a .5 correlation with group separation outdoors in all waves, while group separation outdoors only has a .4 correlation with fixed staff assignment in wave 4b and 5. For face masks and tests it goes up to .2, in wave 5 correlation for Covid-19 tests went up to .3. Further, temperature meaurement for children and staff seems to be highly correlated in all waves, with correlations between .5 and .6. For the other variables, the correlations are usually between -.1 to .1 -Hygiene masks includes both, staff wearing a face mask with other staff/parents as well as staff wearing a face mask with children (range 0 to 2).
-Hygiene contact includes the three items on contact, group separation indoor and outdoor and fixed staff assignment to groups (range 0 to 3).
-Hygiene tests includes Covid-19 tests for children and staff as well as temperature measurement for children and staff (range 0 to 4).
• Appendix Figure A8 is a replication of Figure 4 showing coefficients from models that include all single hygiene variables at once, assuming different effects of every single measure here (generous setting). In the following, we briefly discuss findings from both settings and explain how they influenced our variable selection.
• Considering face masks, the summarized index in Figure A7 provides significant effects of face masks on the number of infections in children (wave 4a, 5) and for the occurrence and number of infections in staff (wave 4b, 5). Disentangeling the mask effect by using both variables in Figure A8, the use of face masks with staff/parents and with children shows that most of the protective effects stems from wearing face masks with children, while the usage of face masks in contact with staff/parents only showed significant negative effects for the number of infections in children in wave 4a. The protective effect on the occurrence of infections in staff in wave 5 found in Figure A7 fails to reach significance in Figure 4. Here, including the single items clearly provides helpful insights.
• When it comes to the index for surface desinfection and regular ventilation (Hygiene desinf. and vent), we cannot find any significant effects when using the index (see Figure A7). This supported our decision not to include surface desinfection in the final model. Including regular ventilation only (see Figure 4) or including both variables (see Figure A8) did not change that picture. Anyway, as regular ventilation was in focus of the debate in Germany, we decided to present the results in Figure 4, but to exclude surface disinfection, as preceding analyses [2] and actual results did not show any substantial results.
• For the contact restriction index, we found negative significant effects for the occurrence of infections in parents (wave 4b) and the number of infections in staff in the index setting (wave 4b, see Figure A7). When adding all (correlated) contact variables at once in the models (see Figure A8), we find a negative effect on the number of infections in children in wave 5 for outdoor group separation, but a positive effect for fixed staff assignment in the same model. In our opinion, this positive finding is best explained by the high correlation of the contact variables mentioned above, since it makes little sense in terms of content. We further find a single negative within effect of outdoor group separation on the occurrence of infections in parents in wave 4b. This effect is also found in Figure A7. The significant effect (see Figure 4) of indoor group separation on the number of infections turns insignificant in Figure A8, but is still found in Figure A7. Overall, as the majority of all significant estimates for contact restriction variables in both our settings are clearly negative, hence, preventive, we consider group separation to be beneficial to some degree, without, however, being able to specify the exact area of effect. These findings in mind, we decided to use the indoor contact variable as a usefull proxy for contact restrictions in the final model, because it showed the highest correlation with the other two contact variables.
• Considering tests, the index setting (see Figure A7) provides significant effects for the number of infections in children (wave 5) and for the number of infections in staff (wave 4b and 5). Both effects make intuitive sense, as tests with children and tests with staff should reveal infections in children and staff.
-Looking at both COVID-19 test variables as single items in the generous setting (see Figure A8) reveals a positive effect of testing staff on infections of staff, which makes perfect sense, but no further effects of staff testing. The positive effects of tests on the number of infections in children in wave 5 and on the number infections in staff in wave 4 found in the index setting fail to reach significance when separated into single items. The corresponding effects point in the same direction, but are not individually significant. The protective effect of the introduction of tests for children on occurrence and number of infections in parents in wave 4a and 4b is only found in Figure A8 and Figure 4, this supports the assumtion of increased attention by parents due to the tests they must perform with their children mentioned in the paper.
-Considering temperature measurement, our in-depth analysis in Figure   A8 provides negative significant within effects of the introduction of temperature measurement for children on the occurrence and number of infections in children in wave 4a, and on the number of infections in staff in wave 5. These results are counterintuitive to some degree, as a positive result in temperature measurement should actually lead to a positive test and thus have a positive effect size, as it would increase the number of infections found, similar to the positive effect of tests in the index setting (see Figure A7). We assume that this result is due to a very limited case number, but also due high correlation of the both temperature measurement variables (See Figure A2). * Case Numbers: Figures A4 to A6 show histograms of the within variance of all time-varying variables per wave. As Figures A4 to A6 show, the estimates for temperature measurement are based on comparatively few observations with within variance, as could be seen in the extremely small tails of the corresponding histograms. In numbers: Only 220 of 4721 centres in wave 4a have any within variance unequal to 0 in temperature measurements for children in wave 4a, and of these 220 centres, only 44 observe any infections in children, while e.g. 1625 centres have a within variance unequal to 0 when it comes to face masks for children in wave 4a (own calculations).
* Correlation: Looking at the other non-significant effects for temperature measurement for staff in Figure A8, e.g. in the first model (occurrance wave 4a), the two temperature measurement variables seem to work in different directions and have comparatively large confidence bounds. The within effect for temperature measurement for staff is comparably large and positive, but not significant, while temperature measurement for children is negative. This finding might result from the high correlation (.5 to .6, see Figure A2) between the two variables. To follow up further, we run an additional model where we include only temperature measurement for children and drop the temperature measurement for staff variable (see Figure A9). Here, the effect for temperature measurement for children is still negative, but not significant.
Hence, when it comes to temperature measurement, we find it somewhat difficult to make any substantive statement here. Moreover, given the very low case numbers, the lack of within variation and high correlation between both measurements, prefer to put the result under great reserve and refrain from including the variables into the final model. Further research is needed here, especially in areas where temperature measurement has been applied more widely. Figure A7: Index Setting: Replication of Figure 4 including all hygiene measures shown in Figure 3 as indices Wave 4a Wave 4b Wave 5 Source: ECEC centre registry, estimates from logit and count models for SARS-CoV-2 infections children, parents and staff. Odds ratios (OR, logit Models) and incidence rate ratios (IRR, count models), significant coefficients (p < 0.05) are printed in opaque with OR/IRR and 95% CI, non significant coefficients are printed in transparent colours. Full models in tables A6, A7 and A8, own calculations.  Source: ECEC centre registry, estimates from logit and count models for SARS-CoV-2 infections children, parents and staff. Odds ratios (OR, logit Models) and incidence rate ratios (IRR, count models), significant coefficients (p < 0.05) are printed in opaque with OR/IRR and 95% CI, non significant coefficients are printed in transparent colours. Full models in tables A9, A10 and A11, own calculations. Figure A9: Generous setting: Replication of Figure 4 including all hygiene measures shown in Figure 3 as single items, includes only temperature measurement for children Source: ECEC centre registry, estimates from logit and count models for SARS-CoV-2 infections children, parents and staff. Odds ratios (OR, logit Models) and incidence rate ratios (IRR, count models), significant coefficients (p < 0.05) are printed in opaque with OR/IRR and 95% CI, non significant coefficients are printed in transparent colours. Full models not shown, own calculations.      Table A3 including all hygiene measures: Logit and count models for infections in children, parents and staff, wave 5